Factores que afectan el impacto pesquero sobre peces cartilaginosos en el sureste español (Mediterráneo Suroccidental)
DOI:
https://doi.org/10.3989/scimar.04025.21APalabras clave:
Área Marina Protegida de Cabo de Palos, conservación, peces cartilaginosos, impacto pesquero, árboles de regresión, algoritmos evolutivos, especies vulnerablesResumen
Proponemos un índice global de impacto basado en la vulnerabilidad relativa de las poblaciones locales de cada una de las especies y la posterior aplicación de árboles de regresión globalmente optimizados con algoritmos evolutivos, para estudiar el impacto de la pesca en los peces cartilaginosos del sureste español. El impacto de la pesca es mucho mayor, dentro de los 11 km de la reserva marina de Cabo de Palos, en aquellas zonas de menos de 40 m de profundidad. El impacto también depende del estado de la mar y el tipo de hábitat. Los hábitats de aguas profundas asociados a sustrato duro y fondo arenoso muestran los máximos impactos, mientras que, tanto los fondos fangosos sublitorales como los hábitats rocosos circalitorales con moderada energía de las corrientes, muestran un menor impacto. Además, se dan cambios a lo largo del ciclo lunar en el impacto de la pesca, lo que significa que existen diferentes patrones diarios, asociados a distintos hábitats, con distinta composición específica. Finalmente, mostramos que la optimización global de los árboles de regresión es esencial para revelar patrones importantes y son una herramienta útil para determinar aquellas áreas mas importantes en términos de protección, teniendo en cuenta, concretamente, la vulnerabilidad de las poblaciones locales.
Descargas
Citas
Baum J.K., Myers R.A., Kehler D.G., Worm B., Harley S.J., Doherty P.A. 2003. Collapse and conservation of shark populations in the Northwest Atlantic. Science 299: 389-392. http://dx.doi.org/10.1126/science.1079777 PMid:12532016
Bigelow K.A., Boggs C.H., He X. 1999. Environmental effects on swordfish and blue shark catch rates in the US North Pacific longline fishery. Fish. Oceanogr. 8: 178-198. http://dx.doi.org/10.1046/j.1365-2419.1999.00105.x
Borregaard M.K., Rahbek, C. 2010. Causality of the relationship between geographic distribution and species abundance. Quart. Rev. Biol. 85: 3-25. http://dx.doi.org/10.1086/650265 PMid:20337258
Casey J.M. Myers R.A. 1998. Near extinction of a large, widely distributed fish. Science 281: 690-692. http://dx.doi.org/10.1126/science.281.5377.690 PMid:9685260
Castro J.I., Woodley C.M., Brudek, R.L. 1999. A preliminary evaluation of the status of shark species. FAO Fisheries Technical Paper 380.
Castro W.L., Pitcher T.J., Pauly, D. 2005. A fuzzy logic expert system to estimate intrinsic extinction vulnerability of marine fishes to fishing. Biol. Conserv. 124: 97-111. http://dx.doi.org/10.1016/j.biocon.2005.01.017
Damalas D., Megalofonou P., Apostolopoulou, M. 2007.Environmental, spatial, temporal and operational effects on swordfish (Xiphias gladius) catch rates of eastern Mediterranean Sea longline fisheries. Fish. Res. 84: 233-246. http://dx.doi.org/10.1016/j.fishres.2006.11.001
Davidson A.D., Boyer A.G., Ki H., Pompa-Mansilla S., Hamilton M.J., Costa D.P., Ceballos G., Brown J.H. 2012. Drivers and hotspots of extinction risk in marine mammals. PNAS 109: 3395-3400. http://dx.doi.org/10.1073/pnas.1121469109 PMid:22308490 PMCid:PMC3295301
Davies C.E., Moss D., O'Hill, M. 2004. EUNIS habitat classification Revised 2004. European Environment Agency, European topic centre on nature protection and biodiversity.
Dulvy N.K., Forrest R.E. 2010. Life histories, population dynamics and extinction risks in chondrichthyans. In: Carrier J., Musick J., Heithaus M. (eds), Sharks and their Relatives II. Biodiversity, Adaptive Physiological Conservation. CRC Press, Boca Raton, pp. 639-679. http://dx.doi.org/10.1201/9781420080483-c17
Dulvy N.K., Reynolds J.D. 2002. Predicting extinction vulnerability in skates. Conserv. Biol. 16: 440-450. http://dx.doi.org/10.1046/j.1523-1739.2002.00416.x
Dulvy N.K., Sadovy Y., Reynolds J.D. 2003. Extinction vulnerability in marine populations. Fish. Fish. 4: 25-64. http://dx.doi.org/10.1046/j.1467-2979.2003.00105.x
Dulvy N.K., Ellis J.R., Goodwin N.B., Grant A., Reynolds J.D., Jennings S. 2004. Methods of assessing extinction risk in marine fishes. Fish. Fish. 5: 255-276. http://dx.doi.org/10.1111/j.1467-2679.2004.00158.x
European Commission 2006. Sensitive and Essential Fish Habitats in the Mediterranean Sea. Rome, pp. 6-10.
Ferretti F., Myers R.A., Serena F., Lotze, H.K. 2008. Loss of large predatory sharks from the Mediterranean Sea. Conserv. Biol. 22: 952-964. http://dx.doi.org/10.1111/j.1523-1739.2008.00938.x PMid:18544092
Frankham R., Ballou J.D., Briscoe D.A. 2002. Introduction to conservation genetics. Cambridge Univ. Press. http://dx.doi.org/10.1017/CBO9780511808999 PMid:12126990
Frisk M.G., Miller T.J., Dulvy N.K. 2005. Life histories and vulnerability to exploitation of cartilaginous fish: Inferences from elasticity, perturbation and phylogenetic analyses. J. Northw. Atl. Fish. Sci. 35: 27-45. http://dx.doi.org/10.2960/J.v35.m514
Froese R., Pauly D. 2011. FishBase. World Wide Web electronic publication. www.fishbase.org, version.
Gouraguine A., Hidalgo M., Moranta J., Bailey D., Ordines F., Guijarro B., Valls M., Barberá C., De Mesa A. 2011. Cartilaginous fish spatial segregation in the western Mediterranean. Sci. Mar. 75: 653-664. http://dx.doi.org/10.3989/scimar.2011.75n4653
Grubinger T., Zeileis A., Pfeiffer K.P. 2011. evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees. In: Working Paper 2011-xx. Working Papers in Economics and Statistics, Research Platform Empirical and Experimental Economics, Universitt Innsbruck.
Hazin F., Burgess G., Carvalho F. 2008. A Shark Attack Outbreak Off Recife, Pernambuco, Brazil: 1992–2006. B. Mar. Sci. 82: 199-212.
Hergstrom K., Niall R. 1990. Presence–absence sampling of two spotted spider mite (Acari: Tetranychidae) in pear orchards. J. Econ. Entomol. 83: 2032-2035.
Hernandez-Milian G., Goetz S., Varela C., Rodriguez J., Romon J., Fuertes J.R., Ulloa E., Tregenza N.J.C., Smerdon A., Otero M.G., Tato V., Wang J., Santos M.B., López A., Lago R., Portela, J., Pierce G.J. 2008. Results of a short study of interactions of cetaceans and longline fisheries in Atlantic waters: environmental correlates of catches and depredation events. Hydrobiologia 612: 251-268. http://dx.doi.org/10.1007/s10750-008-9501-2
Isaac N.J.B., Cowlishaw G. 2004. How species respond to multiple extinction threats. P. Royal Soc. B-Biol. Sci. 271: 1135-1141.
Jennings S., Kaiser M. 1998. The effects of fishing on marine ecosystems. Adv. Mar. Biol. 34: 201-352. http://dx.doi.org/10.1016/S0065-2881(08)60212-6
Jennings S., Greenstreet S.P.R., Reynolds J.D. 1999. Structural change in an exploited fish community: a consequence of differential fishing effects on species with contrasting life histories. J. Anim. Ecol. 68: 617-627. http://dx.doi.org/10.1046/j.1365-2656.1999.00312.x
Kaiser M.J., Collie J.S., Hall S.J., Jennings S. Poiner I.R. 2003. Impacts of fishing gear on marine benthic habitats. In: Sinclair M., Valdimarsson G. (eds), Respons. Fish. Mar. Ecos. pp. 197-217. Rome, FAO.
Krzysztof J.C., Pedrycz W., Swiniarski R.W., Kurgan L.A. 2007. Data Mining: A Knowledge Discovery Approach. Springer-Verlag New York, Inc. Secaucus, New Jersey.
Madsen N. 2007. Selectivity of fishing gears used in the Baltic Sea cod fishery. Rev Fish. Biol. Fish. 4: 517-544. http://dx.doi.org/10.1007/s11160-007-9053-y
Massutí E., Moranta J. 2003. Demersal assemblages and depth distribution ofcartilaginous fish from the continental shelf and slope off the Balearic Islands westernMediterranean. – ICES J. Mar. Sci. 60: 753-766. http://dx.doi.org/10.1016/S1054-3139(03)00089-4
McDowall R.M. 1969. Lunar Rhythms in Aquatic Animals: A General Review. Tuatara 17: 3.
McEachran J.D., Musick J.A. 1975. Distribution and relative abundance of seven species of skates Pisces: Rajidae which occur between Nova Scotia and Cape Hatteras.U.S. Fish. B. 73: 110-136.
Mendoza M. 2007. Decision Trees: a Machine Learning Methodology for characterizing Morphological Patterns resulting from Ecological Adaptations. In: MacLeod N. (ed), Automated Object Identification in Systematics: Theory, Approaches and Applications pp. 261-276. Systematics Association's Special Volume Series, UK. http://dx.doi.org/10.1201/9781420008074.ch15
Mendoza M., García T., Baro J. 2010. Using classification trees to study the effects of fisheries management plans on the yield of Merluccius merluccius Linnaeus, 1758 in the Alboran Sea Western Mediterranean. Fish. Res.102: 191-198. http://dx.doi.org/10.1016/j.fishres.2009.11.012
Mendoza M., Pennino M.G., Bellido J.M. 2011. Tree-Based Machine Learning Analysis for Fisheries Research. In: Intilli J.S. (ed), Fishery Management pp. 25-35. Fish, Fishing and Fisheries Series. Nova Science Publishers.
Millsap B.A., Gore. J.A., Runde D.E., Cerulean S.I. 1990. Setting priorities for the conservation of fish and wildlife species in Florida. Wildlife Monographs.
Moore H.B. 1958. Marine Ecology Wiley, New York.
Nielsen S.E., Johnson C.J., Heard D.C.Boyce M.S. 2005. Can models of presence-absence be used to scale abundance? Two case studies considering extremes in life history. Ecography 28: 197-208. http://dx.doi.org/10.1111/j.0906-7590.2005.04002.x
Pallares P., Garcia-Mamolar J.M. 1985. Efectos de las fases de la luna sobre los rendimientos de la flota atunera tropical espa-ola. International Commission for the Conservation of Atlantic Tunas ICCAT 23: 228-236.
Pauly D., Christensen V., Guénette S., Pitcher T., Sumaila U.R., Walters C., Watson R., Zeller D. 2002. Toward sustainability in world fisheries. Nature 418: 689-695. http://dx.doi.org/10.1038/nature01017 PMid:12167876
Pauly D., Alder J., Bennett E., Christensen V., Tyedmers P., Watson R. 2003. The future for fisheries. Science 302: 1359-1361. http://dx.doi.org/10.1126/science.1088667 PMid:14631031
Pennino M., Mu-oz F., Conesa D., López-Quílez A.Bellido J.M. 2013. Modelling sensitive cartilaginous fish habitats. J. Sea Res. V: 1-25.
Pérez-Ortiz M, Colmenarejo R., Fernández-Caballero J. C., Hervás-Martínez C. 2013. Can Machine Learning Techniques Help to Improve the Common Fisheries Policy? IWANN (2) 2013: 278-286.
Quinlan J.R. 1985. Induction of decision trees. Mach. Learn. 1: 81-106. http://dx.doi.org/10.1007/BF00116251
Reynolds J.D., Jenings S., Dulvy N.K. 2001. Life histories of fishes and population responses to exploitation. In: Reynolds J.D., Mace G.M., Redford K.H., Robinson J.G. (eds), Conservation of Exploited Species pp. 147-168. Cambridge University Press, Cambridge.
Roel B.A. 1987. Demersal communities of the west coast of South Africa. South Afric. J. Mar. Sci. 5: 575–584. http://dx.doi.org/10.2989/025776187784522135
Rogan J., Franklin J., Stow D., Miller J., Roberts D.A., Woodcock C. 2008. Mapping land cover modifications over large areas: A comparison of machine learning techniques, Remote Sens. Environ. 112: 2272-2283. http://dx.doi.org/10.1016/j.rse.2007.10.004
Sadovy Y., Cheung W.L. 2003. Near extinction of a highly fecund fish: the one that nearly got away. Fish. Fish. 4: 86-99. http://dx.doi.org/10.1046/j.1467-2979.2003.00104.x
Stevens J.D., Bonfil R., Dulvy N.K., Walker P.A. 2000. The effects of fishing on sharks, rays, and chimaeras (chondrichthyans), and the implications for marine ecosystems. ICES J. Mar. Sci. 57: 476-494. http://dx.doi.org/10.1006/jmsc.2000.0724
Venier L.A., Fahrig L. 1998. Intraspecific abundance-distribution relationships. Oikos 82: 483-490. http://dx.doi.org/10.2307/3546369
Watling L., Norse E.A. 1998. Disturbance of the seabed by mobile fishing gear: comparison to forest clearcutting. Conserv. Biol. 12: 1180-1197. http://dx.doi.org/10.1046/j.1523-1739.1998.0120061180.x
Wilson S.K., Fisher R., Pratchett M.S., Graham N.A.J., Dulvy N.K., Turner R.A., Cakacaka A., Polunin N.V.C. 2010. Habitat degradation and fishing effects on the size structure of coral reef fish communities. Ecol. Appl. 20: 442-451. http://dx.doi.org/10.1890/08-2205.1 PMid:20405798
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2014 Consejo Superior de Investigaciones Científicas (CSIC)
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
© CSIC. Los originales publicados en las ediciones impresa y electrónica de esta Revista son propiedad del Consejo Superior de Investigaciones Científicas, siendo necesario citar la procedencia en cualquier reproducción parcial o total.Salvo indicación contraria, todos los contenidos de la edición electrónica se distribuyen bajo una licencia de uso y distribución “Creative Commons Reconocimiento 4.0 Internacional ” (CC BY 4.0). Puede consultar desde aquí la versión informativa y el texto legal de la licencia. Esta circunstancia ha de hacerse constar expresamente de esta forma cuando sea necesario.
No se autoriza el depósito en repositorios, páginas web personales o similares de cualquier otra versión distinta a la publicada por el editor.
